KDD2019 Research Track Session RT10: Embeddings II

[まとめページへのリンク](https://qiita.com/kotaaaa/items/4f144d660d504cc091cf)

# Research Track Session RT10: Embeddings II – Summit 2, Ground Level, Egan Center Chair: Jundong Li 

## Individualized Indicator for All: Stock-wise Technical Indicator Optimization with Stock Embedding Zhige Li (Shanghai Jiao Tong University) 
Derek Yang (Tsinghua University); Li Zhao (Microsoft); Jiang Bian (Microsoft); Tao Qin (Microsoft); Tie-Yan Liu (Microsoft) 

### purpose
1, token embedding 
2, analysis of embedding 

Preliminaries about technical analysis 
Introduction and properties
Technical indicators are developed to recognize reading patterns

Indicator-Based Portfolio construction 

- Single indicator 
- Multiple indicators 
Evaluation metrics
Limitation: Unified Transformation 
The stock-wise indicator optimization model 
Distinguish stock properties 
Stocks within the same fun as likely to share some common characteristics 

Stock embedding 
Fund-stock graph construction 

Indicator rescaling  method
Assumption: 

- Constancy and continuity 
Principle: 
- keep the original properties
- Stocks adjustment 

### Experimental insight 
Effectiveness of optimized indicator 

Performance in real-world investing 
Case study:
Different indicator show different sensitives towards different stock 
Similar rescaling weights distributed scales

### Conclusion 

- Leverage the difference in terms of indicator’s stock-wise affinity
- Data mining view to learn the stock representation by mining knowledge repository 
- Proposed a delicately-designed rescaling network, for the purpose of retaining the original properties the indicator

 

## Efficient Global String Kernel with Random Features: Beyond Counting Substructures 
Lingfei Wu (IBM Research); Ian En-Hsu Yen (CMU); Siyu Huo (IBM Research); Liang Zhao (George Mason University); Kun Xu (IBM Research); Liang Ma (IBM Research); Shouling Ji (Zhejiang University); Charu Aggarwal (IBM Research) 

String kernel analysis: Applications
Analysis of large-scale sequential data has been one of the most crucial tasks 

- Bioinformatics 
- Text 
- Audio mining 
String kernels are effective method learning features from the sequence

##### difficulty

- Hardly capture long discriminative patterns 
- Diagonal dominance the kernel matrix
- Experience quadratic complexity in the number of samples 

##### Contribution 

- A family of positive-definite global string kernels 
- Random string embedding 
- Theoretically Show Uniform convergence of RSE

##### Existing approaches for string kernels 
String kernels by counting substructures > Ignore global properties and beed high computation of kernel matrix
Edit distance substitution > Invalid positive definite kernel

##### Key idea 
-Develop a general framework of building string kernel and generating string embedding from edit distance 

Global string kernel using edit distance and random features 

The core task:  to build a positive -definite string kernel utilizing the global alignment measure (via edit distance or Levenshtein distance)
Connection to distance substitution kernel 

Random string embedding: random features of global string kernel
Efficient computation of RSE: use Random Features
Data-independent 
Distance measure
The convergence of random string embedding 
How many random features are required in (10) to have an accurate 

Increasing R help improve the accuracy with linear complexity of R
conclusion

- presented scalable global string kernels
- A simple yet effective way to handle real-world large-scale string data


## HATS: A Hierarchical Sequence-Attention Framework for Inductive Set-of-Sets Embeddings 
Changping Meng (Purdue University); Jiasen Yang (Purdue University); Bruno Ribeiro (Purdue University); Jennifer Neville (Purdue University) 

## typical deep learning tasks need a canonical orientation 
traditional neural networks are permutation aware

set-of-set 
example: Adamic-Adar index: measure the similarity between a pair of nodes in the graph based on the degree of their common neighbors
(since there's no canonical ordering, the data may have..) 


##### learning representation of set-of-set
SOS is a set whose elements are themselves sets.
desired representation function properties
Output: SOS representation 

##### architecture for SOS
intra-set representation 
how to make HATS permutation invariant
which can be estimated using Monte Carlo at test time


##### Experiment models: 
Deepset, MI-CNN, J-lstm, HATS, H-lstm
input: each SOS conatnains4 sets. each set has 10 integers from 0 to 9.
output: regression float output rounded to the nearest integer

##### task: predict Adamic-Adar index between two nodes in a graph 
experiment hyperlink 
In: each set contains m=4
Out: binary label

Task SOS:

- anomaly detection 
- unique count 

##### conclusion 

- set-od set representation need to be permutation invariant and
- have no trivial representation collision 
- HATS learn SOS representation following 2 properties variational learning methods for HATS, giving  state of the art results in multiple tasks


## TUBE: Embedding Behavior Outcomes for Predicting Success 
Daheng Wang (University of Notre Dame); Tianwen Jiang (University of Notre Dame & Harbin Institute of Technology); Nitesh V. Chawla (University of Notre Dame); Meng Jiang (University of Notre Dame) 

##### purpose 

- predicting the success of publishing papers 
- predicting effective medical resource for alleviating symptoms

Given a goal and a plan, how to predict the effectiveness of the plan
task: goal prediction: given plan p predict goals g that is likely to achieve 
context recommendation: given goal and plan recommend item to be added into that maximize effectiveness.

plan effectiveness as point-to-point distance?
point-to-point distance is not effectiveness
Approach: Embeds Behavior oUTcomes TUBE
Approach: optimization 
distance between observed and predicted distribution 
KL-divergence distance 
Approach: negative samples 
experiments: building synthetic datasets 

- simulate "game" scenarios of forming a team of players for passing game stage
experiments: synthetic datasets
experiments: synthetic datasets results
task: goal prediction and context recommendation 


##### conclusion 

- new problems representation learning for behavior context items and goals for success prediction 


## Multi-Relational Classification via Bayesian Ranked Non-Linear Embeddings 
Ahmed Rashed (University of Hildesheim); Josif Grabocka (University of Hildesheim); Lars Schmidt-Thieme (University of Hildesheim) Learning Network-to-Network Model for Content-rich Network Embedding Zhicheng He (Nankai University); Jie Liu (Nankai University); Na Li (Nankai University); Yalou Huang (Nankai University)

##### intro
multi-relational settings: social network, citation networks, biological interactive 

goals: predicting the missing edges

##### main challenges 

- extremely sparse relation 
- attributed entities 
- proposed method bayesian ranked non-linear embedding 

multi-relational classification 

##### related work

- attribute-aware models 
- non-attribute-aware models 
- bayesian ranked non-linear model

step1: non-linear embeddings 
step2: scoring function 
step3: bayesian personalized ranking 

data: Cora, Citeseer, PPI, email-Eu-core

##### conclusion

- propose Bayesian ranked non-linear embeddings(BRNLE)
- easily extendable for an arbitrary number od relation of entities